The problem of financial credit scoring is a very challenging and important financial analysis problem. The main challenge with the credit risk modeling and assessment is that the current models are riddled with uncertainty. The estimation of the probability of default (insolvency), modeling correlation structure for a group of connected borrowers and estimation of amount of correlation are the most important sources of uncertainty that can severely impair the quality of credit risk models.
Many techniques have already been proposed to tackle this problem, ranging from statistical classifiers to decision trees, nearest-neighbor methods and neural networks. Although the latter are powerful pattern recognition techniques, their use for practical problem solving (and credit scoring) is rather limited due to their intrinsic opaque, black box nature. The best known method in the industry is the Fair Isaac Corporation (FICO®) score. The FICO Score® is calculated from several different pieces of credit data in a credit report of a user. The data may be grouped into five categories as shown in FIG. 1. The percentages in the chart in FIG. 1 reflect how important each of the categories is in determining how the FICO score of each user is calculated.
The FICO Score considers both positive and negative information in a credit report. For example, late payments will lower your FICO Score, but establishing or re-establishing a good track record of making payments on time will raise your score. There are several credit reporting companies that work in conjunction with each other, such as Equifax and Transunion to name but a few organizations. However these companies are not able to properly align themselves to a risk management model that matches health based loans. Specifically they do not know the demographics, the place of living, the occupation, the length of employment and dependents of the person. Further in the case of health based services and loans, the risk are increased due to the unhealthy nature of the consumer who is getting the health service and must pay back the loan.
The recent upsurge in health care costs has seen an increased interest in lending to consumers for health services. It is desirable to provide a system and method for determining a health credit score that models the risk of a health care loan to a consumer.